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approaches (based on functional programming abstractions) to optimize the implementation of machine learning models and other digital signal processing algorithms on a specific FPGA architecture to fit within
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this will include: Demonstrated expertise in data analysis and simulation Familiarity with C++; and proficiency in the use of ROOT and Geant4, and interest in machine learning techniques Knowledge
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McGill University | Winnipeg Sargent Park Daniel McIntyre Inkster SE, Manitoba | Canada | about 2 months ago
fluorescence data. Developing machine learning methods to optimize data collection. In addition, the project is committed to developing open source tools that benefit the imaging community. The applicant will
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Council of Canada). The research will focus on applying, developing, and implementing novel statistical methods for causal inference, integrative data analysis, and machine learning with large
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: - To lead the computational part of a collaborative project on AI-assisted design of OPVs - To become knowledgeable in the field of OPVs and the relevant simulations - To learn relevant machine learning
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of Dr Benoit Gosselin (Université Laval), Guillaume Lajoie (UdeM) and Marco Bonizzato (Polytechnique). It integrates the use of machine-learning approaches to optimize neurostimulation, automation